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Microsoft Azure Machine Learning, by Sumit Mund
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Explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicksAbout This Book
- Learn how to build predictive models using a browser such as IE
- Explore different machine learning algorithms available
- Without any prior knowledge and experience get started with predictive analytics with confidence
The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.
What You Will Learn- Learn to use Azure Machine Learning Studio to visualize and pre-process data
- Build models and make predictions using data classification, regression, and clustering algorithms
- Build a basic recommender system
- Deploy your predictive solution as a Web service API
- Integrate R and Python code in your model built with ML Studio
- Explore with more than one case study
This book provides you with the skills necessary to get started with Azure Machine Learning to build predictive models as quickly as possible, in a very intuitive way, whether you are completely new to predictive analysis or an existing practitioner.
The book starts by exploring ML Studio, the browser-based development environment, and explores the first step—data exploration and visualization. You will then build different predictive models using both supervised and unsupervised algorithms, including a simple recommender system. The focus then shifts to learning how to deploy a model to production and publishing it as an API.
The book ends with a couple of case studies using all the concepts and skills you have learned throughout the book to solve real-world problems.
- Sales Rank: #491604 in eBooks
- Published on: 2015-06-16
- Released on: 2015-06-16
- Format: Kindle eBook
About the Author
Sumit Mund
Sumit Mund is a BI/analytics consultant with about a decade of industry experience. He works in his own company, Mund Consulting Ltd, where he is a director and lead consultant. He is an expert in machine learning, predictive analytics, C#, R, and Python programming; he also has an active interest in Artificial Intelligence. He has extensive experience working with most of Microsoft Data Analytics tools and also on Big Data platforms, such as Hadoop and Spark. He is a Microsoft Certified Solution Expert (MCSE in Business Intelligence). Sumit regularly engages on social media platforms through his tweets, blogs, and LinkedIn profile, and often gives talks at industry conferences and local user group meetings.
Most helpful customer reviews
2 of 2 people found the following review helpful.
OK Introduction to MS Machine Learning
By Horseman with a Book
Review of “Microsoft Azure Machine Learning” by Sumit Mund
Chapter 1:
1. Starting from the initial pages, the prose is a bit verbose e.g.
a. “Traditionally, it has been an area for experts. Developing and deploying a predictive modeling solution using machine learning has never been simple and easy, even for experts.” – OK, enough with the “experts” theme. We get it. It’s hard. One sentence would is enough to get the point across. This level of verbosity is a hallmark of the book as there are examples of this overstatement and restatement throughout.
b. “You should not misunderstand that predictive analytics…” should be simply “You should understand…” the double negative is confusing and unnecessary.
c. While not painfully obvious, it is clear that English is not the author’s native language. There are places where an inappropriate word or phrase is used e.g. “This kind of algorithm takes all of the data and groups them into different clusters…” – the use of the word “them” is unusual, a more common usage would be “…groups it into different clusters”. This is jarring to a native English speaking reader.
d. In many places the word “the” is dropped inappropriately – again creating a jarring experience for one fluent in English.
e. Phrases are sometimes mal-formed as if directly translated from a different language.
2. Too much time is spent explaining basic themes such as defining project scope, the relative value and completeness of the data, etc. These things are already known to those of us in the software development field and for those that are professionals in their related engineering fields that would find this tool very useful, restating the obvious borders on insulting.
3. Model reuse – again, too many examples. We get the notion of “code reuse”.
4. Whomever the editor was for this book needs to be reviewed for competency :
a. Missed an OBVIOUS FAIL when starting with the VERY FIRST EXAMPLE in the section entitled “Creating and running an experiment – do it yourself”. Item 1 on the list contains the following text: “…and then choose the Black Experiment …”. I’m pretty sure the author meant “BLANK” not “BLACK”. Oops.
b. Incorrect word used: “A decision tree is a set of questions or decisions and their possible consequences arranged in a hierarchical FISSION”
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